JUDEA PEARL

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Things brings us to part-3 of the lecture, where I will demonstrate how the ideas presented thus far can be used to solve new problems of practical importance. |

This is all history. Now we enter a hypothetical era where representatives of both sides decide to meet and iron out their differences. The tobacco industry concedes that there might be some weak causal link between smoking and cancer and representatives of the health group concede that there might be some weak links to genetic factors. Accordingly, they draw this combined model, and the question boils down to assessing, from the data, the strengths of the various links. They submit the query to a statistician and the answer comes back immediately: IMPOSSIBLE. Meaning: there is no way to estimate the strength from the data, because any data whatsoever can perfectly fit either one of these two extreme models. So they give up, and decide to continue the political battle as usual.

Before parting, a suggestion comes up:
perhaps we can resolve our differences,
if we measure some auxiliary factors,
For example, since the causal link model
is based on the understanding that smoking affects
lung cancer through the accumulation of tar deposits
in the lungs, perhaps we can measure the amount of
tar deposits in the lungs of sampled individuals, and this might provide
the necessary information for quantifying the links?
Both sides agree that this is a reasonable suggestion,
so they submit a new query to the statistician: Can we
find the effect of smoking on cancer assuming that an
intermediate measurement of tar deposits is available???
The statistician comes back with good news: IT IS COMPUTABLE and,
moreover, the solution is given in close mathematical form. HOW?

Our next example illustrates how a long-standing problem is solved
by purely graphical means - proven by the new algebra.
The problem is called THE ADJUSTMENT PROBLEM or "the covariate selection
problem" and represents the practical side of Simpson's paradox.

At the end of these manipulations, we end up with the
answer to our question: "IF X is disconnected from Y, then Z
and _{1}Z are appropriate measurements."
_{2}ENDING STATEMENT I now wish to summarize briefly the central message of this lecture. It is true that testing for cause and effect is difficult. Discovering causes of effects is even more difficult. But causality is not MYSTICAL OR METAPHYSICAL. It can be understood in terms of simple processes, and it can be expressed in a friendly mathematical language, ready for computer analysis. |

Remarks: technical details can be found in

Hard copies of these and other related publications can be obtained from

Prof. Judea Pearl

UCLA Computer Science Department

4532 Boelter Hall

Los Angeles, CA 90095-1596

or download a postscript file from http://singapore.cs.ucla.edu/frl_papers.html.